diff --git a/COLOR_TEST_REPORT.md b/COLOR_TEST_REPORT.md
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+# Jersey Color Detection - VLM Comparison Report
+
+**Date:** 2026-02-24
+**Test set:** 161 basketball images (`basketball_jersery_color_test_files/`)
+
+## Overview
+
+Five tests were run to evaluate how vision-language models describe jersey colors:
+
+| Test | Model | Images | Prompt | Purpose |
+|------|-------|--------|--------|---------|
+| 1 | Qwen2.5-VL-7B (local, llama.cpp) | 161 | Named colors | Baseline color vocabulary |
+| 2 | Gemini 3 Flash (cloud API) | 161 | Named colors | Cloud model color vocabulary |
+| 3 | Qwen3-VL-8B (local, llama.cpp) | 161 | Named colors | Newer local model color vocabulary |
+| 4 | Gemini 3 Flash (cloud API) | 20 (random, seed=42) | Hex codes (jersey only) | Hex color specificity |
+| 5 | Qwen3-VL-8B (local, llama.cpp) | 20 (random, seed=42) | Hex codes (jersey only) | Hex color specificity |
+
+---
+
+## Named Color Vocabulary (Tests 1-3)
+
+### Detection Volume
+
+| Metric | Qwen2.5-VL-7B | Gemini 3 Flash | Qwen3-VL-8B |
+|--------|---------------|----------------|--------------|
+| Jerseys detected | 369 | 453 | 444 |
+| Errors | 0 | 0 | 1 |
+| Avg time/image | 14.9s | 15.9s | 17.0s |
+| Unique jersey colors | 15 | 19 | 15 |
+| Unique number colors | 11 | 15 | 13 |
+| Combined palette size | 15 | 19 | 17 |
+
+Gemini detected the most jerseys (453) and used the broadest color vocabulary (19 terms). Qwen3-VL-8B detected nearly as many jerseys (444) as Gemini but with a vocabulary closer to the older Qwen2.5 model.
+
+### Jersey Color Distribution
+
+| Color | Qwen2.5-VL-7B | Gemini 3 Flash | Qwen3-VL-8B | Notes |
+|-------|---------------|----------------|--------------|-------|
+| white | 84 (22.8%) | 125 (27.6%) | 120 (27.0%) | Top color for all three |
+| blue | 60 (16.3%) | 43 (9.5%) | 69 (15.5%) | Both Qwen models lump blues |
+| green | 48 (13.0%) | 60 (13.2%) | 53 (11.9%) | Consistent across models |
+| black | 31 (8.4%) | 21 (4.6%) | 33 (7.4%) | |
+| purple | 25 (6.8%) | 28 (6.2%) | 30 (6.8%) | Consistent |
+| red | 27 (7.3%) | 22 (4.9%) | 28 (6.3%) | |
+| orange | 24 (6.5%) | 27 (6.0%) | 27 (6.1%) | Very consistent |
+| yellow | 27 (7.3%) | 24 (5.3%) | 26 (5.9%) | |
+| maroon | 14 (3.8%) | 23 (5.1%) | 15 (3.4%) | Gemini uses maroon more |
+| light blue | 6 (1.6%) | 22 (4.9%) | 13 (2.9%) | Gemini distinguishes light blue most |
+| gray/grey | 9 (2.4%) | 12 (2.6%) | 10 (2.3%) | |
+| brown | 6 (1.6%) | 13 (2.9%) | 9 (2.0%) | |
+| teal | 4 (1.1%) | 7 (1.5%) | 7 (1.6%) | |
+| pink | 2 (0.5%) | 2 (0.4%) | 2 (0.5%) | |
+| gold | 2 (0.5%) | 2 (0.4%) | 2 (0.5%) | |
+| navy blue | -- | 11 (2.4%) | -- | Gemini-only |
+| dark blue | -- | 9 (2.0%) | -- | Gemini-only |
+| dark brown | -- | 1 (0.2%) | -- | Gemini-only |
+| navy | -- | 1 (0.2%) | -- | Gemini-only |
+
+### Number Color Distribution
+
+| Color | Qwen2.5-VL-7B | Gemini 3 Flash | Qwen3-VL-8B |
+|-------|---------------|----------------|--------------|
+| white | 195 (52.8%) | 183 (40.4%) | 184 (41.4%) |
+| black | 60 (16.3%) | 40 (8.8%) | 44 (9.9%) |
+| yellow | 39 (10.6%) | 58 (12.8%) | 32 (7.2%) |
+| red | 30 (8.1%) | 44 (9.7%) | 41 (9.2%) |
+| blue | 23 (6.2%) | 39 (8.6%) | 39 (8.8%) |
+| orange | 8 (2.2%) | 21 (4.6%) | 29 (6.5%) |
+| gold | -- | 5 (1.1%) | 21 (4.7%) |
+| dark blue | -- | 14 (3.1%) | 9 (2.0%) |
+| maroon | 2 (0.5%) | 14 (3.1%) | 12 (2.7%) |
+| green | 3 (0.8%) | 13 (2.9%) | 14 (3.2%) |
+| purple | 4 (1.1%) | 11 (2.4%) | 11 (2.5%) |
+| pink | 3 (0.8%) | 6 (1.3%) | 6 (1.4%) |
+| brown | 2 (0.5%) | 2 (0.4%) | -- |
+| grey | -- | 2 (0.4%) | -- |
+| navy blue | -- | 1 (0.2%) | -- |
+| silver | -- | -- | 2 (0.5%) |
+
+### Key Differences in Named Color Mode
+
+1. **Gemini has the richest vocabulary.** It uses 19 distinct jersey color terms vs 15 for both Qwen models. The extras are all blue-shade variants (navy blue, dark blue, navy) and dark brown.
+
+2. **Both Qwen models lump blues together.** Qwen2.5-VL-7B reports 60 "blue" jerseys, Qwen3-VL-8B reports 69. Gemini splits these into blue (43), light blue (22), navy blue (11), dark blue (9), and navy (1) — totaling 86 blue-family detections with much finer granularity.
+
+3. **Qwen3-VL-8B is a modest upgrade over Qwen2.5-VL-7B.** It detects 20% more jerseys (444 vs 369) and uses the same 15 jersey color terms but with a slightly more balanced distribution. It has the same vocabulary as Qwen2.5 but added "dark blue", "silver" to its number color palette.
+
+4. **Gemini detects the most jerseys overall.** 453 vs 444 (Qwen3) vs 369 (Qwen2.5). The two newer models are close, while Qwen2.5 lags behind.
+
+5. **All three models are dominated by basic colors.** White, blue/green, and black account for the majority of detections. None spontaneously uses precise shade names like "crimson", "cobalt", or "forest green".
+
+6. **Qwen3-VL-8B favors "gold" for number colors.** It reported gold 21 times for number colors vs Gemini's 5 and Qwen2.5's 0. This may reflect team-specific coloring (e.g., Lakers gold numbers).
+
+---
+
+## Hex Color Specificity (Tests 4-5)
+
+Both tests used the same 20 random images (seed=42) and evaluated **jersey colors only** (number colors excluded since they are usually primary colors like white or black).
+
+### Summary
+
+| Metric | Gemini 3 Flash | Qwen3-VL-8B |
+|--------|----------------|--------------|
+| Images tested | 20 | 20 |
+| Total jerseys | 56 | 59 |
+| Jersey color values | 56 | 59 |
+| Valid hex codes | 56/56 (100%) | 59/59 (100%) |
+| Unique hex values | 24 | 21 |
+| Specific (distinct shade) | 40 (71.4%) | 37 (62.7%) |
+| Generic (near primary) | 16 (28.6%) | 22 (37.3%) |
+
+### Distance from Nearest Primary Color
+
+| Stat | Gemini 3 Flash | Qwen3-VL-8B |
+|------|----------------|--------------|
+| Min | 0.0 | 0.0 |
+| Avg | 44.5 | 34.5 |
+| Max | 111.0 | 110.7 |
+
+(Scale: 0 = exact primary match. 20 = generic threshold. Higher = more specific.)
+
+### Gemini 3 Flash - Unique Hex Values (24)
+
+| Hex | RGB | Count | Classification |
+|-----|-----|-------|---------------|
+| `#004B23` | (0, 75, 35) | x7 | specific, near green (dark), d=63.5 |
+| `#1A2344` | (26, 35, 68) | x2 | specific, near navy, d=74.2 |
+| `#1E4BA1` | (30, 75, 161) | x1 | specific, near navy, d=87.3 |
+| `#2B231D` | (43, 35, 29) | x1 | specific, near black, d=62.6 |
+| `#3D2B1F` | (61, 43, 31) | x1 | specific, near black, d=80.8 |
+| `#461D7C` | (70, 29, 124) | x1 | specific, near purple, d=65.0 |
+| `#4B2E83` | (75, 46, 131) | x5 | specific, near purple, d=70.2 |
+| `#701112` | (112, 17, 18) | x1 | specific, near maroon, d=29.5 |
+| `#7BAFD4` | (123, 175, 212) | x3 | specific, near silver, d=73.8 |
+| `#990000` | (153, 0, 0) | x2 | specific, near maroon, d=25.0 |
+| `#A9A9A9` | (169, 169, 169) | x1 | specific, near silver, d=39.8 |
+| `#C41230` | (196, 18, 48) | x1 | specific, near brown, d=39.7 |
+| `#D11111` | (209, 17, 17) | x2 | specific, near red, d=51.9 |
+| `#D32F2F` | (211, 47, 47) | x2 | specific, near brown, d=46.5 |
+| `#E31837` | (227, 24, 55) | x1 | specific, near brown, d=65.9 |
+| `#E31B23` | (227, 27, 35) | x1 | specific, near red, d=52.3 |
+| `#E3242B` | (227, 36, 43) | x2 | specific, near brown, d=62.3 |
+| `#E6E600` | (230, 230, 0) | x1 | specific, near gold, d=29.2 |
+| `#E8E8E8` | (232, 232, 232) | x1 | specific, near white, d=39.8 |
+| `#E91E63` | (233, 30, 99) | x1 | specific, near brown, d=89.5 |
+| `#F06292` | (240, 98, 146) | x2 | specific, near pink, d=111.0 |
+| `#F57C00` | (245, 124, 0) | x1 | specific, near orange, d=42.2 |
+| `#FFCD00` | (255, 205, 0) | x1 | GENERIC, near gold, d=10.0 |
+| `#FFFFFF` | (255, 255, 255) | x15 | GENERIC, near white, d=0.0 |
+
+### Qwen3-VL-8B - Unique Hex Values (21)
+
+| Hex | RGB | Count | Classification |
+|-----|-----|-------|---------------|
+| `#000000` | (0, 0, 0) | x1 | GENERIC, near black, d=0.0 |
+| `#006400` | (0, 100, 0) | x10 | specific, near green (dark), d=28.0 |
+| `#191970` | (25, 25, 112) | x1 | specific, near navy, d=38.8 |
+| `#19418A` | (25, 65, 138) | x1 | specific, near navy, d=70.4 |
+| `#3D2B21` | (61, 43, 33) | x2 | specific, near black, d=81.6 |
+| `#66B2FF` | (102, 178, 255) | x3 | specific, near silver, d=110.7 |
+| `#6A0DAD` | (106, 13, 173) | x6 | specific, near purple, d=51.7 |
+| `#8B0000` | (139, 0, 0) | x1 | GENERIC, near maroon, d=11.0 |
+| `#A9A9A9` | (169, 169, 169) | x1 | specific, near silver, d=39.8 |
+| `#B22234` | (178, 34, 52) | x2 | GENERIC, near brown, d=18.2 |
+| `#D32F2F` | (211, 47, 47) | x3 | specific, near brown, d=46.5 |
+| `#D60000` | (214, 0, 0) | x3 | specific, near red, d=41.0 |
+| `#DC143C` | (220, 20, 60) | x2 | specific, near brown, d=61.9 |
+| `#F5F5DC` | (245, 245, 220) | x2 | specific, near white, d=37.7 |
+| `#F5F5F5` | (245, 245, 245) | x1 | GENERIC, near white, d=17.3 |
+| `#FF0000` | (255, 0, 0) | x1 | GENERIC, near red, d=0.0 |
+| `#FF6347` | (255, 99, 71) | x1 | specific, near orange, d=96.9 |
+| `#FF69B4` | (255, 105, 180) | x2 | specific, near pink, d=90.0 |
+| `#FFD700` | (255, 215, 0) | x1 | GENERIC, near gold, d=0.0 |
+| `#FFFF00` | (255, 255, 0) | x1 | GENERIC, near yellow, d=0.0 |
+| `#FFFFFF` | (255, 255, 255) | x14 | GENERIC, near white, d=0.0 |
+
+### Notable Findings
+
+- **Both models can produce valid hex codes.** 100% of returned values were valid hex in both cases.
+
+- **Gemini is more specific overall.** 71.4% of its jersey hex codes were distinct shades vs 62.7% for Qwen3. Gemini also produced more unique hex values (24 vs 21) and had a higher average distance from primaries (44.5 vs 34.5).
+
+- **Gemini uses more varied shades of each color family.** For red-family jerseys, Gemini returned 8 distinct hex values (`#701112`, `#990000`, `#C41230`, `#D11111`, `#D32F2F`, `#E31837`, `#E31B23`, `#E3242B`). Qwen3 returned 6 (`#8B0000`, `#B22234`, `#D32F2F`, `#D60000`, `#DC143C`, `#FF0000`), including two exact primaries.
+
+- **Qwen3 reuses hex values more heavily.** `#006400` (dark green) appeared 10 times and `#FFFFFF` 14 times — two values account for 41% of all results. Gemini's most repeated value was `#FFFFFF` at 15 times (27%), with better spread across other shades.
+
+- **White dominates both models.** `#FFFFFF` was the single most common value for both (Gemini: x15, Qwen3: x14), which is expected given white jerseys are the most common in basketball.
+
+- **Both models share some exact hex codes.** `#3D2B21` (dark brown), `#A9A9A9` (dark silver/gray), and `#D32F2F` (medium red) appeared in both models' outputs, suggesting some convergence on certain color estimations.
+
+---
+
+## Conclusions
+
+1. **For basic color categorization, all three models work.** If you only need to distinguish "white vs dark vs colored" jerseys, any will do. Gemini offers slightly finer granularity with its blue-shade vocabulary (navy blue, dark blue, navy).
+
+2. **Gemini detects the most jerseys per image** (2.81 avg), followed closely by Qwen3-VL-8B (2.76 avg), with Qwen2.5-VL-7B trailing (2.29 avg).
+
+3. **Qwen3-VL-8B is a solid upgrade over Qwen2.5-VL-7B** for detection volume (+20% more jerseys) while maintaining the same color vocabulary. It runs locally without cloud API costs, making it a good default choice.
+
+4. **Hex color prompting works for jersey body colors.** Both models return specific hex shades the majority of the time (Gemini 71%, Qwen3 63%). Gemini produces more varied and specific shades, while Qwen3 tends to reuse a smaller set of hex values.
+
+5. **Neither model is a reliable colorimeter.** The hex values should be treated as rough shade estimates, not pixel-accurate measurements. For precise color matching, traditional computer vision (e.g., sampling pixels from the detected jersey region) would be more reliable.
+
+6. **Recommendation:** Use named-color prompts for general jersey classification. Reserve hex-color prompts for use cases where distinguishing similar shades matters (e.g., telling apart two teams that both wear "blue"). Gemini gives the best hex specificity but requires a cloud API; Qwen3-VL-8B is a capable local alternative.
\ No newline at end of file
diff --git a/color_test_results.md b/color_test_results.md
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+#Qwen2.5-VL-7B Model Results:
+
+======================================================================
+COLOR VARIETY SUMMARY
+======================================================================
+Images processed: 161
+Total jerseys detected: 369
+Errors: 0
+Total time: 2397.7s (14.9s avg)
+
+--- Jersey Colors (15 unique) ---
+ white 84 ##################################################
+ blue 60 ##################################################
+ green 48 ################################################
+ black 31 ###############################
+ yellow 27 ###########################
+ red 27 ###########################
+ purple 25 #########################
+ orange 24 ########################
+ maroon 14 ##############
+ gray 9 #########
+ light blue 6 ######
+ brown 6 ######
+ teal 4 ####
+ pink 2 ##
+ gold 2 ##
+
+--- Number Colors (11 unique) ---
+ white 195 ##################################################
+ black 60 ##################################################
+ yellow 39 #######################################
+ red 30 ##############################
+ blue 23 #######################
+ orange 8 ########
+ purple 4 ####
+ pink 3 ###
+ green 3 ###
+ brown 2 ##
+ maroon 2 ##
+
+--- Combined Color Palette (15 unique values) ---
+ black jersey: 31 number: 60
+ blue jersey: 60 number: 23
+ brown jersey: 6 number: 2
+ gold jersey: 2 number: 0
+ gray jersey: 9 number: 0
+ green jersey: 48 number: 3
+ light blue jersey: 6 number: 0
+ maroon jersey: 14 number: 2
+ orange jersey: 24 number: 8
+ pink jersey: 2 number: 3
+ purple jersey: 25 number: 4
+ red jersey: 27 number: 30
+ teal jersey: 4 number: 0
+ white jersey: 84 number:195
+ yellow jersey: 27 number: 39
+
+
+#Gemini 3 Flash Results:
+
+======================================================================
+COLOR VARIETY SUMMARY (gemini-3-flash-preview)
+======================================================================
+Images processed: 161
+Total jerseys detected: 453
+Errors: 0
+Total time: 2560.0s (15.9s avg)
+
+--- Jersey Colors (19 unique) ---
+ white 125 ##################################################
+ green 60 ##################################################
+ blue 43 ###########################################
+ purple 28 ############################
+ orange 27 ###########################
+ yellow 24 ########################
+ maroon 23 #######################
+ light blue 22 ######################
+ red 22 ######################
+ black 21 #####################
+ brown 13 #############
+ grey 12 ############
+ navy blue 11 ###########
+ dark blue 9 #########
+ teal 7 #######
+ pink 2 ##
+ gold 2 ##
+ dark brown 1 #
+ navy 1 #
+
+--- Number Colors (15 unique) ---
+ white 183 ##################################################
+ yellow 58 ##################################################
+ red 44 ############################################
+ black 40 ########################################
+ blue 39 #######################################
+ orange 21 #####################
+ dark blue 14 ##############
+ maroon 14 ##############
+ green 13 #############
+ purple 11 ###########
+ pink 6 ######
+ gold 5 #####
+ brown 2 ##
+ grey 2 ##
+ navy blue 1 #
+
+--- Combined Color Palette (19 unique values) ---
+ black jersey: 21 number: 40
+ blue jersey: 43 number: 39
+ brown jersey: 13 number: 2
+ dark blue jersey: 9 number: 14
+ dark brown jersey: 1 number: 0
+ gold jersey: 2 number: 5
+ green jersey: 60 number: 13
+ grey jersey: 12 number: 2
+ light blue jersey: 22 number: 0
+ maroon jersey: 23 number: 14
+ navy jersey: 1 number: 0
+ navy blue jersey: 11 number: 1
+ orange jersey: 27 number: 21
+ pink jersey: 2 number: 6
+ purple jersey: 28 number: 11
+ red jersey: 22 number: 44
+ teal jersey: 7 number: 0
+ white jersey:125 number:183
+ yellow jersey: 24 number: 58
+
+
+#Qwen3-VL-8B Model Results:
+
+======================================================================
+COLOR VARIETY SUMMARY
+======================================================================
+Images processed: 161
+Total jerseys detected: 444
+Errors: 1
+Total time: 2738.7s (17.0s avg)
+
+--- Jersey Colors (15 unique) ---
+ white 120 ##################################################
+ blue 69 ##################################################
+ green 53 ##################################################
+ black 33 #################################
+ purple 30 ##############################
+ red 28 ############################
+ orange 27 ###########################
+ yellow 26 ##########################
+ maroon 15 ###############
+ light blue 13 #############
+ gray 10 ##########
+ brown 9 #########
+ teal 7 #######
+ pink 2 ##
+ gold 2 ##
+
+--- Number Colors (13 unique) ---
+ white 184 ##################################################
+ black 44 ############################################
+ red 41 #########################################
+ blue 39 #######################################
+ yellow 32 ################################
+ orange 29 #############################
+ gold 21 #####################
+ green 14 ##############
+ maroon 12 ############
+ purple 11 ###########
+ dark blue 9 #########
+ pink 6 ######
+ silver 2 ##
+
+--- Combined Color Palette (17 unique values) ---
+ black jersey: 33 number: 44
+ blue jersey: 69 number: 39
+ brown jersey: 9 number: 0
+ dark blue jersey: 0 number: 9
+ gold jersey: 2 number: 21
+ gray jersey: 10 number: 0
+ green jersey: 53 number: 14
+ light blue jersey: 13 number: 0
+ maroon jersey: 15 number: 12
+ orange jersey: 27 number: 29
+ pink jersey: 2 number: 6
+ purple jersey: 30 number: 11
+ red jersey: 28 number: 41
+ silver jersey: 0 number: 2
+ teal jersey: 7 number: 0
+ white jersey:120 number:184
+ yellow jersey: 26 number: 32
+
+
+
+#Gemini 3 Flash (Hex Colors, random sample of 10 images) Results:
+
+Test params: test_hex_color_specificity.py --sample 20 --seed 42
+
+======================================================================
+HEX COLOR SPECIFICITY ANALYSIS
+======================================================================
+Model: gemini-3-flash-preview
+Images tested: 20 (seed=42)
+Total jerseys: 56
+Total jersey color values: 56
+Errors: 0
+
+Valid hex codes: 56/56
+
+--- Specificity Breakdown ---
+ Generic (near a pure primary): 16 (28.6%)
+ Specific (distinct shade): 40 (71.4%)
+
+--- Unique Hex Values (24) ---
+ #004B23 RGB( 0, 75, 35) HSL(148.0,100.0%,14.7%) x7 [specific, near green (dark), d=63.5]
+ #1A2344 RGB( 26, 35, 68) HSL(227.1,44.7%,18.4%) x2 [specific, near navy, d=74.2]
+ #1E4BA1 RGB( 30, 75,161) HSL(219.4,68.6%,37.5%) x1 [specific, near navy, d=87.3]
+ #2B231D RGB( 43, 35, 29) HSL( 25.7,19.4%,14.1%) x1 [specific, near black, d=62.6]
+ #3D2B1F RGB( 61, 43, 31) HSL( 24.0,32.6%,18.0%) x1 [specific, near black, d=80.8]
+ #461D7C RGB( 70, 29,124) HSL(265.9,62.1%,30.0%) x1 [specific, near purple, d=65.0]
+ #4B2E83 RGB( 75, 46,131) HSL(260.5,48.0%,34.7%) x5 [specific, near purple, d=70.2]
+ #701112 RGB(112, 17, 18) HSL(359.4,73.6%,25.3%) x1 [specific, near maroon, d=29.5]
+ #7BAFD4 RGB(123,175,212) HSL(204.9,50.9%,65.7%) x3 [specific, near silver, d=73.8]
+ #990000 RGB(153, 0, 0) HSL( 0.0,100.0%,30.0%) x2 [specific, near maroon, d=25.0]
+ #A9A9A9 RGB(169,169,169) HSL( 0.0, 0.0%,66.3%) x1 [specific, near silver, d=39.8]
+ #C41230 RGB(196, 18, 48) HSL(349.9,83.2%,42.0%) x1 [specific, near brown, d=39.7]
+ #D11111 RGB(209, 17, 17) HSL( 0.0,85.0%,44.3%) x2 [specific, near red, d=51.9]
+ #D32F2F RGB(211, 47, 47) HSL( 0.0,65.1%,50.6%) x2 [specific, near brown, d=46.5]
+ #E31837 RGB(227, 24, 55) HSL(350.8,80.9%,49.2%) x1 [specific, near brown, d=65.9]
+ #E31B23 RGB(227, 27, 35) HSL(357.6,78.7%,49.8%) x1 [specific, near red, d=52.3]
+ #E3242B RGB(227, 36, 43) HSL(357.8,77.3%,51.6%) x2 [specific, near brown, d=62.3]
+ #E6E600 RGB(230,230, 0) HSL( 60.0,100.0%,45.1%) x1 [specific, near gold, d=29.2]
+ #E8E8E8 RGB(232,232,232) HSL( 0.0, 0.0%,91.0%) x1 [specific, near white, d=39.8]
+ #E91E63 RGB(233, 30, 99) HSL(339.6,82.2%,51.6%) x1 [specific, near brown, d=89.5]
+ #F06292 RGB(240, 98,146) HSL(339.7,82.6%,66.3%) x2 [specific, near pink, d=111.0]
+ #F57C00 RGB(245,124, 0) HSL( 30.4,100.0%,48.0%) x1 [specific, near orange, d=42.2]
+ #FFCD00 RGB(255,205, 0) HSL( 48.2,100.0%,50.0%) x1 [GENERIC, near gold, d=10.0]
+ #FFFFFF RGB(255,255,255) HSL( 0.0, 0.0%,100.0%) x15 [GENERIC, near white, d=0.0]
+
+--- Distance from Nearest Primary ---
+ Min: 0.0 Avg: 44.5 Max: 111.0
+ (Higher = more specific. Threshold for 'generic' = 20)
+
+--- Verdict ---
+ MIXED results (71% specific).
+ The model sometimes returns specific shades but often falls back to primaries.
+
+
+
+#Qwen3-VL-8B (Hex Colors, random sample of 10 images) Results:
+
+Test params: test_hex_color_specificity_llama.py --sample 20 --seed 42
+
+======================================================================
+HEX COLOR SPECIFICITY ANALYSIS
+======================================================================
+Model: unsloth_Qwen3-VL-8B-Instruct-GGUF_Qwen3-VL-8B-Instruct-BF16
+Server: http://agx:8080
+Images tested: 20 (seed=42)
+Total jerseys: 59
+Total jersey color values: 59
+Errors: 0
+
+Valid hex codes: 59/59
+
+--- Specificity Breakdown ---
+ Generic (near a pure primary): 22 (37.3%)
+ Specific (distinct shade): 37 (62.7%)
+
+--- Unique Hex Values (21) ---
+ #000000 RGB( 0, 0, 0) HSL( 0.0, 0.0%, 0.0%) x1 [GENERIC, near black, d=0.0]
+ #006400 RGB( 0,100, 0) HSL(120.0,100.0%,19.6%) x10 [specific, near green (dark), d=28.0]
+ #191970 RGB( 25, 25,112) HSL(240.0,63.5%,26.9%) x1 [specific, near navy, d=38.8]
+ #19418A RGB( 25, 65,138) HSL(218.8,69.3%,32.0%) x1 [specific, near navy, d=70.4]
+ #3D2B21 RGB( 61, 43, 33) HSL( 21.4,29.8%,18.4%) x2 [specific, near black, d=81.6]
+ #66B2FF RGB(102,178,255) HSL(210.2,100.0%,70.0%) x3 [specific, near silver, d=110.7]
+ #6A0DAD RGB(106, 13,173) HSL(274.9,86.0%,36.5%) x6 [specific, near purple, d=51.7]
+ #8B0000 RGB(139, 0, 0) HSL( 0.0,100.0%,27.3%) x1 [GENERIC, near maroon, d=11.0]
+ #A9A9A9 RGB(169,169,169) HSL( 0.0, 0.0%,66.3%) x1 [specific, near silver, d=39.8]
+ #B22234 RGB(178, 34, 52) HSL(352.5,67.9%,41.6%) x2 [GENERIC, near brown, d=18.2]
+ #D32F2F RGB(211, 47, 47) HSL( 0.0,65.1%,50.6%) x3 [specific, near brown, d=46.5]
+ #D60000 RGB(214, 0, 0) HSL( 0.0,100.0%,42.0%) x3 [specific, near red, d=41.0]
+ #DC143C RGB(220, 20, 60) HSL(348.0,83.3%,47.1%) x2 [specific, near brown, d=61.9]
+ #F5F5DC RGB(245,245,220) HSL( 60.0,55.6%,91.2%) x2 [specific, near white, d=37.7]
+ #F5F5F5 RGB(245,245,245) HSL( 0.0, 0.0%,96.1%) x1 [GENERIC, near white, d=17.3]
+ #FF0000 RGB(255, 0, 0) HSL( 0.0,100.0%,50.0%) x1 [GENERIC, near red, d=0.0]
+ #FF6347 RGB(255, 99, 71) HSL( 9.1,100.0%,63.9%) x1 [specific, near orange, d=96.9]
+ #FF69B4 RGB(255,105,180) HSL(330.0,100.0%,70.6%) x2 [specific, near pink, d=90.0]
+ #FFD700 RGB(255,215, 0) HSL( 50.6,100.0%,50.0%) x1 [GENERIC, near gold, d=0.0]
+ #FFFF00 RGB(255,255, 0) HSL( 60.0,100.0%,50.0%) x1 [GENERIC, near yellow, d=0.0]
+ #FFFFFF RGB(255,255,255) HSL( 0.0, 0.0%,100.0%) x14 [GENERIC, near white, d=0.0]
+
+--- Distance from Nearest Primary ---
+ Min: 0.0 Avg: 34.5 Max: 110.7
+ (Higher = more specific. Threshold for 'generic' = 20)
+
+--- Verdict ---
+ MIXED results (63% specific).
+ The model sometimes returns specific shades but often falls back to primaries.
+
+
+
diff --git a/jersey_prompt_hex_color.txt b/jersey_prompt_hex_color.txt
new file mode 100644
index 0000000..4a8d733
--- /dev/null
+++ b/jersey_prompt_hex_color.txt
@@ -0,0 +1,50 @@
+You are an expert at detecting sports jerseys in images. Carefully examine the provided image and identify all visible sports jerseys.
+
+CRITICAL INSTRUCTIONS:
+1. ONLY detect jerseys that are CLEARLY VISIBLE in the image
+2. ONLY include jersey numbers that you can ACTUALLY READ in the image
+3. If you CANNOT see any jerseys, you MUST return {"jerseys": []}
+4. DO NOT make up, imagine, or guess jersey numbers that aren't visible
+5. DO NOT include jerseys if you cannot clearly see the number
+
+COLOR INSTRUCTIONS:
+- Report jersey_color and number_color as HEX color codes (e.g. "#8B0000", "#1E3A5F")
+- Do NOT use generic color names like "red", "blue", "white"
+- Estimate the SPECIFIC shade you see in the image as precisely as possible
+- For example: dark maroon should be "#800000", not "#FF0000"
+- Royal blue should be "#4169E1", not "#0000FF"
+
+RESPONSE FORMAT:
+Respond ONLY with a valid JSON object. No explanations, no markdown, no extra text.
+
+Use DOUBLE QUOTES (") for all JSON keys and string values.
+
+The JSON must have a single key "jerseys" with an array of dictionaries.
+
+Each dictionary must have exactly these three keys:
+- "jersey_number": The number on the jersey (as a string, only if clearly visible)
+- "jersey_color": The primary color of the jersey as a HEX code (e.g. "#8B0000")
+- "number_color": The color of the number on the jersey as a HEX code (e.g. "#FFFFFF")
+
+Example response for an image WITH visible jerseys:
+{
+ "jerseys": [
+ {
+ "jersey_number": "101",
+ "jersey_color": "#8B0000",
+ "number_color": "#F5F5DC"
+ },
+ {
+ "jersey_number": "142",
+ "jersey_color": "#1E3A5F",
+ "number_color": "#DAA520"
+ }
+ ]
+}
+
+Example response for an image WITHOUT jerseys or with unclear numbers:
+{"jerseys": []}
+
+REMEMBER: Only include jerseys with numbers you can ACTUALLY SEE in the image. When in doubt, return empty array.
+
+Now analyze the image and return the JSON object.
\ No newline at end of file
diff --git a/test_color_variety.py b/test_color_variety.py
new file mode 100644
index 0000000..c8bb828
--- /dev/null
+++ b/test_color_variety.py
@@ -0,0 +1,151 @@
+#!/usr/bin/env python3
+"""
+Test script to discover the variety of colors a VLM returns for jersey detection.
+
+Submits all test images to the VLM and tallies every unique jersey_color and
+number_color value, producing a summary of the model's color vocabulary.
+
+Usage:
+ python test_color_variety.py
+"""
+
+import json
+import os
+import re
+import sys
+import time
+from collections import Counter
+from pathlib import Path
+
+import cv2
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+from scan_utils.llama_cpp_client import LlamaCppClient
+
+SERVER_URL = "http://agx:8080"
+IMAGES_DIR = os.path.join(os.path.dirname(__file__), "basketball_jersery_color_test_files")
+PROMPT_FILE = os.path.join(os.path.dirname(__file__), "jersey_prompt.txt")
+
+
+def clean_response(text: str) -> str:
+ """Remove think tags and markdown code blocks from model output."""
+ cleaned = re.sub(r'.*?', '', text, flags=re.DOTALL | re.IGNORECASE)
+ cleaned = re.sub(r'\u25c1think\u25b7.*?\u25c1/think\u25b7', '', cleaned, flags=re.DOTALL)
+ cleaned = re.sub(r'', '', cleaned, flags=re.IGNORECASE)
+ cleaned = re.sub(r'\u25c1/?think\u25b7', '', cleaned, flags=re.IGNORECASE)
+
+ json_block = re.search(r'```(?:json)?\s*\n?(.*?)\n?```', cleaned, flags=re.DOTALL | re.IGNORECASE)
+ if json_block:
+ cleaned = json_block.group(1)
+ else:
+ cleaned = re.sub(r'```(?:json)?', '', cleaned, flags=re.IGNORECASE)
+
+ return cleaned.strip()
+
+
+def main():
+ # Load prompt
+ with open(PROMPT_FILE, 'r') as f:
+ prompt = f.read()
+
+ # Gather image files (extensions OpenCV can handle)
+ valid_extensions = {'.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.webp'}
+ image_files = sorted([
+ p for p in Path(IMAGES_DIR).iterdir()
+ if p.suffix.lower() in valid_extensions
+ ])
+
+ skipped = sorted([
+ p.name for p in Path(IMAGES_DIR).iterdir()
+ if p.is_file() and p.suffix.lower() not in valid_extensions
+ ])
+
+ print(f"Images to process: {len(image_files)}")
+ if skipped:
+ print(f"Skipping {len(skipped)} unsupported files: {', '.join(skipped)}")
+ print(f"Server: {SERVER_URL}")
+ print(f"Prompt: {PROMPT_FILE} ({len(prompt)} chars)")
+ print("=" * 70)
+
+ client = LlamaCppClient(base_url=SERVER_URL)
+
+ jersey_color_counter = Counter()
+ number_color_counter = Counter()
+ total_jerseys = 0
+ errors = 0
+ start_all = time.time()
+
+ for i, image_path in enumerate(image_files, 1):
+ print(f"[{i}/{len(image_files)}] {image_path.name} ... ", end="", flush=True)
+
+ image = cv2.imread(str(image_path))
+ if image is None:
+ print("SKIP (failed to load)")
+ errors += 1
+ continue
+
+ message = client.create_multimodal_message(role="user", content=prompt, images=[image])
+
+ try:
+ t0 = time.time()
+ response = client.chat_completion(messages=[message], temperature=0.1, max_tokens=1000)
+ elapsed = time.time() - t0
+
+ response_text = response['choices'][0]['message']['content']
+ cleaned = clean_response(response_text)
+ result = json.loads(cleaned)
+ jerseys = result.get('jerseys', [])
+
+ colors_found = []
+ for j in jerseys:
+ jc = j.get('jersey_color', '').strip().lower()
+ nc = j.get('number_color', '').strip().lower()
+ if jc:
+ jersey_color_counter[jc] += 1
+ if nc:
+ number_color_counter[nc] += 1
+ colors_found.append(f"{jc}/{nc}")
+ total_jerseys += 1
+
+ print(f"{len(jerseys)} jersey(s) in {elapsed:.1f}s {', '.join(colors_found) if colors_found else '(none)'}")
+
+ except (json.JSONDecodeError, KeyError, IndexError) as e:
+ print(f"PARSE ERROR: {e}")
+ errors += 1
+ except Exception as e:
+ print(f"ERROR: {e}")
+ errors += 1
+
+ total_time = time.time() - start_all
+
+ # --- Summary ---
+ print()
+ print("=" * 70)
+ print("COLOR VARIETY SUMMARY")
+ print("=" * 70)
+ print(f"Images processed: {len(image_files)}")
+ print(f"Total jerseys detected: {total_jerseys}")
+ print(f"Errors: {errors}")
+ print(f"Total time: {total_time:.1f}s ({total_time / len(image_files):.1f}s avg)")
+
+ print(f"\n--- Jersey Colors ({len(jersey_color_counter)} unique) ---")
+ for color, count in jersey_color_counter.most_common():
+ bar = "#" * min(count, 50)
+ print(f" {color:25s} {count:4d} {bar}")
+
+ print(f"\n--- Number Colors ({len(number_color_counter)} unique) ---")
+ for color, count in number_color_counter.most_common():
+ bar = "#" * min(count, 50)
+ print(f" {color:25s} {count:4d} {bar}")
+
+ # Combined unique palette
+ all_colors = sorted(set(jersey_color_counter.keys()) | set(number_color_counter.keys()))
+ print(f"\n--- Combined Color Palette ({len(all_colors)} unique values) ---")
+ for color in all_colors:
+ jc = jersey_color_counter.get(color, 0)
+ nc = number_color_counter.get(color, 0)
+ print(f" {color:25s} jersey:{jc:3d} number:{nc:3d}")
+
+
+if __name__ == '__main__':
+ main()
\ No newline at end of file
diff --git a/test_color_variety_gemini.py b/test_color_variety_gemini.py
new file mode 100644
index 0000000..a11731e
--- /dev/null
+++ b/test_color_variety_gemini.py
@@ -0,0 +1,270 @@
+#!/usr/bin/env python3
+"""
+Test script to discover the variety of colors the Gemini 3 Flash VLM returns
+for jersey detection.
+
+Submits all test images to the Gemini API and tallies every unique jersey_color
+and number_color value, producing a summary of the model's color vocabulary.
+
+Usage:
+ python test_color_variety_gemini.py
+"""
+
+import base64
+import json
+import os
+import re
+import sys
+import time
+from collections import Counter
+from pathlib import Path
+
+import cv2
+import requests
+
+GEMINI_MODEL = "gemini-3-flash-preview"
+API_URL = f"https://generativelanguage.googleapis.com/v1beta/models/{GEMINI_MODEL}:generateContent"
+
+IMAGES_DIR = os.path.join(os.path.dirname(__file__), "basketball_jersery_color_test_files")
+PROMPT_FILE = os.path.join(os.path.dirname(__file__), "jersey_prompt.txt")
+API_KEY_FILE = os.path.join(os.path.dirname(__file__), "gemini_api_key.txt")
+
+
+def load_api_key() -> str:
+ with open(API_KEY_FILE, 'r') as f:
+ return f.read().strip()
+
+
+def clean_response(text: str) -> str:
+ """Remove think tags and markdown code blocks from model output."""
+ cleaned = re.sub(r'.*?', '', text, flags=re.DOTALL | re.IGNORECASE)
+ cleaned = re.sub(r'', '', cleaned, flags=re.IGNORECASE)
+
+ json_block = re.search(r'```(?:json)?\s*\n?(.*?)\n?```', cleaned, flags=re.DOTALL | re.IGNORECASE)
+ if json_block:
+ cleaned = json_block.group(1)
+ else:
+ cleaned = re.sub(r'```(?:json)?', '', cleaned, flags=re.IGNORECASE)
+
+ return cleaned.strip()
+
+
+def salvage_jerseys(text: str) -> list[dict]:
+ """Extract complete jersey objects from truncated JSON using regex.
+
+ When the response is cut off mid-JSON, json.loads() fails. We can still
+ recover every fully-formed jersey object that was returned before the
+ truncation point.
+ """
+ pattern = re.compile(
+ r'\{\s*'
+ r'"jersey_number"\s*:\s*"[^"]*"\s*,\s*'
+ r'"jersey_color"\s*:\s*"([^"]*)"\s*,\s*'
+ r'"number_color"\s*:\s*"([^"]*)"\s*'
+ r'\}',
+ re.DOTALL,
+ )
+ jerseys = []
+ for m in pattern.finditer(text):
+ jerseys.append({
+ 'jersey_color': m.group(1),
+ 'number_color': m.group(2),
+ })
+ return jerseys
+
+
+def encode_image(image_path: str) -> tuple[str, str]:
+ """Read an image file and return (base64_data, mime_type)."""
+ ext = Path(image_path).suffix.lower()
+ mime_map = {
+ '.jpg': 'image/jpeg',
+ '.jpeg': 'image/jpeg',
+ '.png': 'image/png',
+ '.webp': 'image/webp',
+ '.bmp': 'image/bmp',
+ '.tiff': 'image/tiff',
+ }
+ mime_type = mime_map.get(ext, 'image/jpeg')
+
+ with open(image_path, 'rb') as f:
+ data = base64.b64encode(f.read()).decode('utf-8')
+
+ return data, mime_type
+
+
+MAX_RETRIES = 3
+RETRY_BACKOFF = [2, 5, 10] # seconds between retries
+
+
+def call_gemini(api_key: str, image_path: str, prompt: str) -> dict:
+ """Send an image + prompt to the Gemini API and return parsed JSON."""
+ image_data, mime_type = encode_image(image_path)
+
+ payload = {
+ "contents": [{
+ "parts": [
+ {
+ "inline_data": {
+ "mime_type": mime_type,
+ "data": image_data,
+ }
+ },
+ {
+ "text": prompt,
+ }
+ ]
+ }],
+ "generationConfig": {
+ "temperature": 0.1,
+ "maxOutputTokens": 8192,
+ "responseMimeType": "application/json",
+ }
+ }
+
+ for attempt in range(MAX_RETRIES):
+ response = requests.post(
+ API_URL,
+ headers={
+ "x-goog-api-key": api_key,
+ "Content-Type": "application/json",
+ },
+ json=payload,
+ )
+
+ if response.status_code >= 500 and attempt < MAX_RETRIES - 1:
+ wait = RETRY_BACKOFF[attempt]
+ print(f"HTTP {response.status_code}, retry in {wait}s ... ", end="", flush=True)
+ time.sleep(wait)
+ continue
+
+ response.raise_for_status()
+ return response.json()
+
+ # Should not reach here, but just in case
+ response.raise_for_status()
+ return response.json()
+
+
+def main():
+ api_key = load_api_key()
+
+ with open(PROMPT_FILE, 'r') as f:
+ prompt = f.read()
+
+ # Gather image files (extensions the API can handle)
+ valid_extensions = {'.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.webp'}
+ image_files = sorted([
+ p for p in Path(IMAGES_DIR).iterdir()
+ if p.suffix.lower() in valid_extensions
+ ])
+
+ skipped = sorted([
+ p.name for p in Path(IMAGES_DIR).iterdir()
+ if p.is_file() and p.suffix.lower() not in valid_extensions
+ ])
+
+ print(f"Model: {GEMINI_MODEL}")
+ print(f"Images to process: {len(image_files)}")
+ if skipped:
+ print(f"Skipping {len(skipped)} unsupported files: {', '.join(skipped)}")
+ print(f"Prompt: {PROMPT_FILE} ({len(prompt)} chars)")
+ print("=" * 70)
+
+ jersey_color_counter = Counter()
+ number_color_counter = Counter()
+ total_jerseys = 0
+ errors = 0
+ start_all = time.time()
+
+ for i, image_path in enumerate(image_files, 1):
+ print(f"[{i}/{len(image_files)}] {image_path.name} ... ", end="", flush=True)
+
+ try:
+ t0 = time.time()
+ resp = call_gemini(api_key, str(image_path), prompt)
+ elapsed = time.time() - t0
+
+ # Extract text from Gemini response
+ text = resp['candidates'][0]['content']['parts'][0]['text']
+ cleaned = clean_response(text)
+ result = json.loads(cleaned)
+ jerseys = result.get('jerseys', [])
+
+ colors_found = []
+ for j in jerseys:
+ jc = j.get('jersey_color', '').strip().lower()
+ nc = j.get('number_color', '').strip().lower()
+ if jc:
+ jersey_color_counter[jc] += 1
+ if nc:
+ number_color_counter[nc] += 1
+ colors_found.append(f"{jc}/{nc}")
+ total_jerseys += 1
+
+ print(f"{len(jerseys)} jersey(s) in {elapsed:.1f}s {', '.join(colors_found) if colors_found else '(none)'}")
+
+ except (json.JSONDecodeError, KeyError, IndexError) as e:
+ raw = ""
+ try:
+ raw = resp['candidates'][0]['content']['parts'][0]['text']
+ except Exception:
+ pass
+ # Try to salvage complete jersey objects from truncated JSON
+ salvaged = salvage_jerseys(raw) if raw else []
+ if salvaged:
+ colors_found = []
+ for j in salvaged:
+ jc = j.get('jersey_color', '').strip().lower()
+ nc = j.get('number_color', '').strip().lower()
+ if jc:
+ jersey_color_counter[jc] += 1
+ if nc:
+ number_color_counter[nc] += 1
+ colors_found.append(f"{jc}/{nc}")
+ total_jerseys += 1
+ print(f"TRUNCATED, salvaged {len(salvaged)} jersey(s) in {elapsed:.1f}s {', '.join(colors_found)}")
+ else:
+ print(f"PARSE ERROR: {e}")
+ if raw:
+ print(f" raw: {raw[:200]}")
+ errors += 1
+ except requests.exceptions.HTTPError as e:
+ print(f"HTTP ERROR: {e}")
+ errors += 1
+ except Exception as e:
+ print(f"ERROR: {e}")
+ errors += 1
+
+ total_time = time.time() - start_all
+
+ # --- Summary ---
+ print()
+ print("=" * 70)
+ print(f"COLOR VARIETY SUMMARY ({GEMINI_MODEL})")
+ print("=" * 70)
+ print(f"Images processed: {len(image_files)}")
+ print(f"Total jerseys detected: {total_jerseys}")
+ print(f"Errors: {errors}")
+ print(f"Total time: {total_time:.1f}s ({total_time / len(image_files):.1f}s avg)")
+
+ print(f"\n--- Jersey Colors ({len(jersey_color_counter)} unique) ---")
+ for color, count in jersey_color_counter.most_common():
+ bar = "#" * min(count, 50)
+ print(f" {color:25s} {count:4d} {bar}")
+
+ print(f"\n--- Number Colors ({len(number_color_counter)} unique) ---")
+ for color, count in number_color_counter.most_common():
+ bar = "#" * min(count, 50)
+ print(f" {color:25s} {count:4d} {bar}")
+
+ # Combined unique palette
+ all_colors = sorted(set(jersey_color_counter.keys()) | set(number_color_counter.keys()))
+ print(f"\n--- Combined Color Palette ({len(all_colors)} unique values) ---")
+ for color in all_colors:
+ jc = jersey_color_counter.get(color, 0)
+ nc = number_color_counter.get(color, 0)
+ print(f" {color:25s} jersey:{jc:3d} number:{nc:3d}")
+
+
+if __name__ == '__main__':
+ main()
\ No newline at end of file
diff --git a/test_hex_color_specificity.py b/test_hex_color_specificity.py
new file mode 100644
index 0000000..fc5f8f5
--- /dev/null
+++ b/test_hex_color_specificity.py
@@ -0,0 +1,355 @@
+#!/usr/bin/env python3
+"""
+Test whether the Gemini 3 Flash VLM can return specific hex color codes
+rather than generic named colors.
+
+Sends a random sample of 10 images using a hex-color prompt, then analyzes
+how specific the returned colors actually are by comparing them against
+a set of known "pure primary" hex values.
+
+Usage:
+ python test_hex_color_specificity.py
+ python test_hex_color_specificity.py --sample 20
+ python test_hex_color_specificity.py --seed 42
+"""
+
+import argparse
+import base64
+import colorsys
+import json
+import math
+import os
+import random
+import re
+import time
+from pathlib import Path
+
+import requests
+
+GEMINI_MODEL = "gemini-3-flash-preview"
+API_URL = f"https://generativelanguage.googleapis.com/v1beta/models/{GEMINI_MODEL}:generateContent"
+
+IMAGES_DIR = os.path.join(os.path.dirname(__file__), "basketball_jersery_color_test_files")
+PROMPT_FILE = os.path.join(os.path.dirname(__file__), "jersey_prompt_hex_color.txt")
+API_KEY_FILE = os.path.join(os.path.dirname(__file__), "gemini_api_key.txt")
+
+# Pure primary / basic colors that indicate the model is NOT being specific
+PRIMARY_COLORS = {
+ "#FF0000": "red",
+ "#00FF00": "green",
+ "#0000FF": "blue",
+ "#FFFF00": "yellow",
+ "#FF00FF": "magenta",
+ "#00FFFF": "cyan",
+ "#FFFFFF": "white",
+ "#000000": "black",
+ "#808080": "gray",
+ "#FFA500": "orange",
+ "#800080": "purple",
+ "#FFC0CB": "pink",
+ "#A52A2A": "brown",
+ "#800000": "maroon",
+ "#008000": "green (dark)",
+ "#000080": "navy",
+ "#C0C0C0": "silver",
+ "#FFD700": "gold",
+}
+
+# Distance threshold: how close to a primary a hex must be to count as "generic"
+# In RGB space (0-255 per channel), 20 is very close
+GENERIC_DISTANCE_THRESHOLD = 20
+
+
+def hex_to_rgb(h: str) -> tuple[int, int, int] | None:
+ """Parse a hex color string to (r, g, b). Returns None if invalid."""
+ h = h.strip().lstrip('#')
+ if len(h) == 6:
+ try:
+ return (int(h[0:2], 16), int(h[2:4], 16), int(h[4:6], 16))
+ except ValueError:
+ return None
+ if len(h) == 3:
+ try:
+ return (int(h[0]*2, 16), int(h[1]*2, 16), int(h[2]*2, 16))
+ except ValueError:
+ return None
+ return None
+
+
+def rgb_distance(a: tuple[int, int, int], b: tuple[int, int, int]) -> float:
+ """Euclidean distance between two RGB colors."""
+ return math.sqrt(sum((x - y) ** 2 for x, y in zip(a, b)))
+
+
+def rgb_to_hsl(r: int, g: int, b: int) -> tuple[float, float, float]:
+ """Convert RGB (0-255) to HSL (h: 0-360, s: 0-100, l: 0-100)."""
+ h, l, s = colorsys.rgb_to_hls(r / 255, g / 255, b / 255)
+ return round(h * 360, 1), round(s * 100, 1), round(l * 100, 1)
+
+
+def classify_color(hex_str: str) -> dict:
+ """Classify a hex color as generic/primary or specific.
+
+ Returns a dict with:
+ valid: bool - whether the hex code is a valid color
+ hex: str - normalized hex code
+ rgb: tuple - (r, g, b)
+ hsl: tuple - (h, s, l)
+ is_generic: bool - True if the color is a pure primary / basic color
+ nearest_primary: str - the closest primary color name
+ primary_distance: float - RGB distance to the nearest primary
+ """
+ rgb = hex_to_rgb(hex_str)
+ if rgb is None:
+ return {'valid': False, 'hex': hex_str, 'reason': 'invalid hex'}
+
+ normalized = f"#{rgb[0]:02X}{rgb[1]:02X}{rgb[2]:02X}"
+ hsl = rgb_to_hsl(*rgb)
+
+ # Find nearest primary
+ best_name = "unknown"
+ best_dist = float('inf')
+ for phex, pname in PRIMARY_COLORS.items():
+ prgb = hex_to_rgb(phex)
+ d = rgb_distance(rgb, prgb)
+ if d < best_dist:
+ best_dist = d
+ best_name = pname
+
+ is_generic = best_dist < GENERIC_DISTANCE_THRESHOLD
+
+ return {
+ 'valid': True,
+ 'hex': normalized,
+ 'rgb': rgb,
+ 'hsl': hsl,
+ 'is_generic': is_generic,
+ 'nearest_primary': best_name,
+ 'primary_distance': round(best_dist, 1),
+ }
+
+
+def load_api_key() -> str:
+ with open(API_KEY_FILE, 'r') as f:
+ return f.read().strip()
+
+
+def clean_response(text: str) -> str:
+ cleaned = re.sub(r'.*?', '', text, flags=re.DOTALL | re.IGNORECASE)
+ cleaned = re.sub(r'', '', cleaned, flags=re.IGNORECASE)
+ json_block = re.search(r'```(?:json)?\s*\n?(.*?)\n?```', cleaned, flags=re.DOTALL | re.IGNORECASE)
+ if json_block:
+ cleaned = json_block.group(1)
+ else:
+ cleaned = re.sub(r'```(?:json)?', '', cleaned, flags=re.IGNORECASE)
+ return cleaned.strip()
+
+
+def salvage_jerseys(text: str) -> list[dict]:
+ """Extract complete jersey objects from truncated JSON."""
+ pattern = re.compile(
+ r'\{\s*'
+ r'"jersey_number"\s*:\s*"[^"]*"\s*,\s*'
+ r'"jersey_color"\s*:\s*"([^"]*)"\s*,\s*'
+ r'"number_color"\s*:\s*"([^"]*)"\s*'
+ r'\}',
+ re.DOTALL,
+ )
+ return [{'jersey_color': m.group(1), 'number_color': m.group(2)} for m in pattern.finditer(text)]
+
+
+def encode_image(image_path: str) -> tuple[str, str]:
+ ext = Path(image_path).suffix.lower()
+ mime_map = {'.jpg': 'image/jpeg', '.jpeg': 'image/jpeg', '.png': 'image/png',
+ '.webp': 'image/webp', '.bmp': 'image/bmp', '.tiff': 'image/tiff'}
+ mime_type = mime_map.get(ext, 'image/jpeg')
+ with open(image_path, 'rb') as f:
+ data = base64.b64encode(f.read()).decode('utf-8')
+ return data, mime_type
+
+
+MAX_RETRIES = 3
+RETRY_BACKOFF = [2, 5, 10]
+
+
+def call_gemini(api_key: str, image_path: str, prompt: str) -> dict:
+ image_data, mime_type = encode_image(image_path)
+ payload = {
+ "contents": [{"parts": [
+ {"inline_data": {"mime_type": mime_type, "data": image_data}},
+ {"text": prompt},
+ ]}],
+ "generationConfig": {
+ "temperature": 0.1,
+ "maxOutputTokens": 8192,
+ "responseMimeType": "application/json",
+ }
+ }
+ for attempt in range(MAX_RETRIES):
+ response = requests.post(
+ API_URL,
+ headers={"x-goog-api-key": api_key, "Content-Type": "application/json"},
+ json=payload,
+ )
+ if response.status_code >= 500 and attempt < MAX_RETRIES - 1:
+ wait = RETRY_BACKOFF[attempt]
+ print(f"HTTP {response.status_code}, retry in {wait}s ... ", end="", flush=True)
+ time.sleep(wait)
+ continue
+ response.raise_for_status()
+ return response.json()
+ response.raise_for_status()
+ return response.json()
+
+
+def main():
+ parser = argparse.ArgumentParser(description="Test hex color specificity from Gemini VLM")
+ parser.add_argument('--sample', type=int, default=10, help='Number of random images to test (default: 10)')
+ parser.add_argument('--seed', type=int, default=None, help='Random seed for reproducibility')
+ args = parser.parse_args()
+
+ api_key = load_api_key()
+
+ with open(PROMPT_FILE, 'r') as f:
+ prompt = f.read()
+
+ # Gather and sample image files
+ valid_extensions = {'.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.webp'}
+ all_images = sorted([
+ p for p in Path(IMAGES_DIR).iterdir()
+ if p.suffix.lower() in valid_extensions
+ ])
+
+ seed = args.seed if args.seed is not None else random.randint(0, 99999)
+ rng = random.Random(seed)
+ sample_size = min(args.sample, len(all_images))
+ sample_images = rng.sample(all_images, sample_size)
+ sample_images.sort()
+
+ print(f"Model: {GEMINI_MODEL}")
+ print(f"Prompt: {PROMPT_FILE}")
+ print(f"Sample: {sample_size} images (seed={seed})")
+ print(f"Selected: {', '.join(p.name for p in sample_images)}")
+ print("=" * 70)
+
+ # Collect all color classifications
+ all_colors = [] # list of dicts with image, field, hex, classification
+ total_jerseys = 0
+ errors = 0
+
+ for i, image_path in enumerate(sample_images, 1):
+ print(f"\n[{i}/{sample_size}] {image_path.name}")
+
+ try:
+ t0 = time.time()
+ resp = call_gemini(api_key, str(image_path), prompt)
+ elapsed = time.time() - t0
+
+ text = resp['candidates'][0]['content']['parts'][0]['text']
+ cleaned = clean_response(text)
+
+ try:
+ result = json.loads(cleaned)
+ jerseys = result.get('jerseys', [])
+ except json.JSONDecodeError:
+ jerseys = salvage_jerseys(cleaned)
+ if jerseys:
+ print(f" (truncated response, salvaged {len(jerseys)} jersey(s))")
+
+ print(f" {len(jerseys)} jersey(s) detected in {elapsed:.1f}s")
+
+ for j in jerseys:
+ total_jerseys += 1
+ raw_hex = j.get('jersey_color', '')
+ c = classify_color(raw_hex)
+ c['image'] = image_path.name
+ c['field'] = 'jersey_color'
+ c['raw'] = raw_hex
+ all_colors.append(c)
+
+ if not c['valid']:
+ status = f" INVALID ({raw_hex})"
+ elif c['is_generic']:
+ status = f" GENERIC ~{c['nearest_primary']}"
+ else:
+ status = f" SPECIFIC (nearest: {c['nearest_primary']}, dist={c['primary_distance']})"
+
+ print(f" jersey: {raw_hex:10s} -> {status}")
+
+ except requests.exceptions.HTTPError as e:
+ print(f" HTTP ERROR: {e}")
+ errors += 1
+ except Exception as e:
+ print(f" ERROR: {e}")
+ errors += 1
+
+ # --- Analysis ---
+ print()
+ print("=" * 70)
+ print("HEX COLOR SPECIFICITY ANALYSIS")
+ print("=" * 70)
+ print(f"Model: {GEMINI_MODEL}")
+ print(f"Images tested: {sample_size} (seed={seed})")
+ print(f"Total jerseys: {total_jerseys}")
+ print(f"Total jersey color values: {len(all_colors)}")
+ print(f"Errors: {errors}")
+
+ valid_colors = [c for c in all_colors if c['valid']]
+ invalid_colors = [c for c in all_colors if not c['valid']]
+
+ print(f"\nValid hex codes: {len(valid_colors)}/{len(all_colors)}")
+ if invalid_colors:
+ print(f"Invalid values ({len(invalid_colors)}):")
+ for c in invalid_colors:
+ print(f" {c['image']} {c['field']}: {c['raw']}")
+
+ if not valid_colors:
+ print("\nNo valid hex colors returned. The model may not support hex output.")
+ return
+
+ generic = [c for c in valid_colors if c['is_generic']]
+ specific = [c for c in valid_colors if not c['is_generic']]
+
+ pct_specific = len(specific) / len(valid_colors) * 100
+
+ print(f"\n--- Specificity Breakdown ---")
+ print(f" Generic (near a pure primary): {len(generic):3d} ({100 - pct_specific:.1f}%)")
+ print(f" Specific (distinct shade): {len(specific):3d} ({pct_specific:.1f}%)")
+
+ # Show unique hex values returned
+ unique_hexes = sorted(set(c['hex'] for c in valid_colors))
+ print(f"\n--- Unique Hex Values ({len(unique_hexes)}) ---")
+ for h in unique_hexes:
+ rgb = hex_to_rgb(h)
+ hsl = rgb_to_hsl(*rgb)
+ cl = classify_color(h)
+ tag = "GENERIC" if cl['is_generic'] else "specific"
+ count = sum(1 for c in valid_colors if c['hex'] == h)
+ print(f" {h} RGB({rgb[0]:3d},{rgb[1]:3d},{rgb[2]:3d}) "
+ f"HSL({hsl[0]:5.1f},{hsl[1]:4.1f}%,{hsl[2]:4.1f}%) "
+ f"x{count} [{tag}, near {cl['nearest_primary']}, d={cl['primary_distance']}]")
+
+ # Distance statistics
+ distances = [c['primary_distance'] for c in valid_colors]
+ avg_dist = sum(distances) / len(distances)
+ min_dist = min(distances)
+ max_dist = max(distances)
+ print(f"\n--- Distance from Nearest Primary ---")
+ print(f" Min: {min_dist:.1f} Avg: {avg_dist:.1f} Max: {max_dist:.1f}")
+ print(f" (Higher = more specific. Threshold for 'generic' = {GENERIC_DISTANCE_THRESHOLD})")
+
+ # Verdict
+ print(f"\n--- Verdict ---")
+ if pct_specific >= 80:
+ print(f" The model returns SPECIFIC hex colors ({pct_specific:.0f}% distinct shades).")
+ print(f" It is capable of estimating precise colors from images.")
+ elif pct_specific >= 40:
+ print(f" MIXED results ({pct_specific:.0f}% specific).")
+ print(f" The model sometimes returns specific shades but often falls back to primaries.")
+ else:
+ print(f" The model mostly returns GENERIC primary colors ({pct_specific:.0f}% specific).")
+ print(f" Hex codes are largely just the standard primary color equivalents.")
+
+
+if __name__ == '__main__':
+ main()
\ No newline at end of file
diff --git a/test_hex_color_specificity_llama.py b/test_hex_color_specificity_llama.py
new file mode 100644
index 0000000..7c6a925
--- /dev/null
+++ b/test_hex_color_specificity_llama.py
@@ -0,0 +1,316 @@
+#!/usr/bin/env python3
+"""
+Test whether a local llama.cpp VLM can return specific hex color codes
+rather than generic named colors.
+
+Sends a random sample of 10 images using a hex-color prompt, then analyzes
+how specific the returned colors actually are by comparing them against
+a set of known "pure primary" hex values.
+
+Usage:
+ python test_hex_color_specificity_llama.py
+ python test_hex_color_specificity_llama.py --sample 20
+ python test_hex_color_specificity_llama.py --seed 42
+ python test_hex_color_specificity_llama.py --server-url http://hitagi:8080
+"""
+
+import argparse
+import colorsys
+import json
+import math
+import os
+import random
+import re
+import sys
+import time
+from pathlib import Path
+
+import cv2
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+from scan_utils.llama_cpp_client import LlamaCppClient
+
+SERVER_URL = "http://agx:8080"
+IMAGES_DIR = os.path.join(os.path.dirname(__file__), "basketball_jersery_color_test_files")
+PROMPT_FILE = os.path.join(os.path.dirname(__file__), "jersey_prompt_hex_color.txt")
+
+# Pure primary / basic colors that indicate the model is NOT being specific
+PRIMARY_COLORS = {
+ "#FF0000": "red",
+ "#00FF00": "green",
+ "#0000FF": "blue",
+ "#FFFF00": "yellow",
+ "#FF00FF": "magenta",
+ "#00FFFF": "cyan",
+ "#FFFFFF": "white",
+ "#000000": "black",
+ "#808080": "gray",
+ "#FFA500": "orange",
+ "#800080": "purple",
+ "#FFC0CB": "pink",
+ "#A52A2A": "brown",
+ "#800000": "maroon",
+ "#008000": "green (dark)",
+ "#000080": "navy",
+ "#C0C0C0": "silver",
+ "#FFD700": "gold",
+}
+
+# Distance threshold: how close to a primary a hex must be to count as "generic"
+# In RGB space (0-255 per channel), 20 is very close
+GENERIC_DISTANCE_THRESHOLD = 20
+
+
+def hex_to_rgb(h: str) -> tuple[int, int, int] | None:
+ """Parse a hex color string to (r, g, b). Returns None if invalid."""
+ h = h.strip().lstrip('#')
+ if len(h) == 6:
+ try:
+ return (int(h[0:2], 16), int(h[2:4], 16), int(h[4:6], 16))
+ except ValueError:
+ return None
+ if len(h) == 3:
+ try:
+ return (int(h[0]*2, 16), int(h[1]*2, 16), int(h[2]*2, 16))
+ except ValueError:
+ return None
+ return None
+
+
+def rgb_distance(a: tuple[int, int, int], b: tuple[int, int, int]) -> float:
+ """Euclidean distance between two RGB colors."""
+ return math.sqrt(sum((x - y) ** 2 for x, y in zip(a, b)))
+
+
+def rgb_to_hsl(r: int, g: int, b: int) -> tuple[float, float, float]:
+ """Convert RGB (0-255) to HSL (h: 0-360, s: 0-100, l: 0-100)."""
+ h, l, s = colorsys.rgb_to_hls(r / 255, g / 255, b / 255)
+ return round(h * 360, 1), round(s * 100, 1), round(l * 100, 1)
+
+
+def classify_color(hex_str: str) -> dict:
+ """Classify a hex color as generic/primary or specific."""
+ rgb = hex_to_rgb(hex_str)
+ if rgb is None:
+ return {'valid': False, 'hex': hex_str, 'reason': 'invalid hex'}
+
+ normalized = f"#{rgb[0]:02X}{rgb[1]:02X}{rgb[2]:02X}"
+ hsl = rgb_to_hsl(*rgb)
+
+ best_name = "unknown"
+ best_dist = float('inf')
+ for phex, pname in PRIMARY_COLORS.items():
+ prgb = hex_to_rgb(phex)
+ d = rgb_distance(rgb, prgb)
+ if d < best_dist:
+ best_dist = d
+ best_name = pname
+
+ is_generic = best_dist < GENERIC_DISTANCE_THRESHOLD
+
+ return {
+ 'valid': True,
+ 'hex': normalized,
+ 'rgb': rgb,
+ 'hsl': hsl,
+ 'is_generic': is_generic,
+ 'nearest_primary': best_name,
+ 'primary_distance': round(best_dist, 1),
+ }
+
+
+def clean_response(text: str) -> str:
+ cleaned = re.sub(r'.*?', '', text, flags=re.DOTALL | re.IGNORECASE)
+ cleaned = re.sub(r'\u25c1think\u25b7.*?\u25c1/think\u25b7', '', cleaned, flags=re.DOTALL)
+ cleaned = re.sub(r'', '', cleaned, flags=re.IGNORECASE)
+ cleaned = re.sub(r'\u25c1/?think\u25b7', '', cleaned, flags=re.IGNORECASE)
+ json_block = re.search(r'```(?:json)?\s*\n?(.*?)\n?```', cleaned, flags=re.DOTALL | re.IGNORECASE)
+ if json_block:
+ cleaned = json_block.group(1)
+ else:
+ cleaned = re.sub(r'```(?:json)?', '', cleaned, flags=re.IGNORECASE)
+ return cleaned.strip()
+
+
+def salvage_jerseys(text: str) -> list[dict]:
+ """Extract complete jersey objects from truncated JSON."""
+ pattern = re.compile(
+ r'\{\s*'
+ r'"jersey_number"\s*:\s*"[^"]*"\s*,\s*'
+ r'"jersey_color"\s*:\s*"([^"]*)"\s*,\s*'
+ r'"number_color"\s*:\s*"([^"]*)"\s*'
+ r'\}',
+ re.DOTALL,
+ )
+ return [{'jersey_color': m.group(1), 'number_color': m.group(2)} for m in pattern.finditer(text)]
+
+
+def main():
+ parser = argparse.ArgumentParser(description="Test hex color specificity from local llama.cpp VLM")
+ parser.add_argument('--sample', type=int, default=10, help='Number of random images to test (default: 10)')
+ parser.add_argument('--seed', type=int, default=None, help='Random seed for reproducibility')
+ parser.add_argument('--server-url', default=SERVER_URL, help=f'llama.cpp server URL (default: {SERVER_URL})')
+ args = parser.parse_args()
+
+ with open(PROMPT_FILE, 'r') as f:
+ prompt = f.read()
+
+ client = LlamaCppClient(base_url=args.server_url)
+
+ # Detect model name from server
+ model_name = "unknown"
+ try:
+ models = client.get_models()
+ if 'data' in models and len(models['data']) > 0:
+ model_id = models['data'][0].get('id', 'unknown')
+ model_name = os.path.splitext(os.path.basename(model_id))[0] if model_id else "unknown"
+ except Exception:
+ pass
+
+ # Gather and sample image files
+ valid_extensions = {'.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.webp'}
+ all_images = sorted([
+ p for p in Path(IMAGES_DIR).iterdir()
+ if p.suffix.lower() in valid_extensions
+ ])
+
+ seed = args.seed if args.seed is not None else random.randint(0, 99999)
+ rng = random.Random(seed)
+ sample_size = min(args.sample, len(all_images))
+ sample_images = rng.sample(all_images, sample_size)
+ sample_images.sort()
+
+ print(f"Model: {model_name}")
+ print(f"Server: {args.server_url}")
+ print(f"Prompt: {PROMPT_FILE}")
+ print(f"Sample: {sample_size} images (seed={seed})")
+ print(f"Selected: {', '.join(p.name for p in sample_images)}")
+ print("=" * 70)
+
+ # Collect all color classifications
+ all_colors = []
+ total_jerseys = 0
+ errors = 0
+
+ for i, image_path in enumerate(sample_images, 1):
+ print(f"\n[{i}/{sample_size}] {image_path.name}")
+
+ image = cv2.imread(str(image_path))
+ if image is None:
+ print(f" SKIP (failed to load)")
+ errors += 1
+ continue
+
+ try:
+ t0 = time.time()
+
+ message = client.create_multimodal_message(role="user", content=prompt, images=[image])
+ response = client.chat_completion(messages=[message], temperature=0.1, max_tokens=1000)
+ elapsed = time.time() - t0
+
+ response_text = response['choices'][0]['message']['content']
+ cleaned = clean_response(response_text)
+
+ try:
+ result = json.loads(cleaned)
+ jerseys = result.get('jerseys', [])
+ except json.JSONDecodeError:
+ jerseys = salvage_jerseys(cleaned)
+ if jerseys:
+ print(f" (truncated response, salvaged {len(jerseys)} jersey(s))")
+
+ print(f" {len(jerseys)} jersey(s) detected in {elapsed:.1f}s")
+
+ for j in jerseys:
+ total_jerseys += 1
+ raw_hex = j.get('jersey_color', '')
+ c = classify_color(raw_hex)
+ c['image'] = image_path.name
+ c['field'] = 'jersey_color'
+ c['raw'] = raw_hex
+ all_colors.append(c)
+
+ if not c['valid']:
+ status = f" INVALID ({raw_hex})"
+ elif c['is_generic']:
+ status = f" GENERIC ~{c['nearest_primary']}"
+ else:
+ status = f" SPECIFIC (nearest: {c['nearest_primary']}, dist={c['primary_distance']})"
+
+ print(f" jersey: {raw_hex:10s} -> {status}")
+
+ except Exception as e:
+ print(f" ERROR: {e}")
+ errors += 1
+
+ # --- Analysis ---
+ print()
+ print("=" * 70)
+ print("HEX COLOR SPECIFICITY ANALYSIS")
+ print("=" * 70)
+ print(f"Model: {model_name}")
+ print(f"Server: {args.server_url}")
+ print(f"Images tested: {sample_size} (seed={seed})")
+ print(f"Total jerseys: {total_jerseys}")
+ print(f"Total jersey color values: {len(all_colors)}")
+ print(f"Errors: {errors}")
+
+ valid_colors = [c for c in all_colors if c['valid']]
+ invalid_colors = [c for c in all_colors if not c['valid']]
+
+ print(f"\nValid hex codes: {len(valid_colors)}/{len(all_colors)}")
+ if invalid_colors:
+ print(f"Invalid values ({len(invalid_colors)}):")
+ for c in invalid_colors:
+ print(f" {c['image']} {c['field']}: {c['raw']}")
+
+ if not valid_colors:
+ print("\nNo valid hex colors returned. The model may not support hex output.")
+ return
+
+ generic = [c for c in valid_colors if c['is_generic']]
+ specific = [c for c in valid_colors if not c['is_generic']]
+
+ pct_specific = len(specific) / len(valid_colors) * 100
+
+ print(f"\n--- Specificity Breakdown ---")
+ print(f" Generic (near a pure primary): {len(generic):3d} ({100 - pct_specific:.1f}%)")
+ print(f" Specific (distinct shade): {len(specific):3d} ({pct_specific:.1f}%)")
+
+ # Show unique hex values returned
+ unique_hexes = sorted(set(c['hex'] for c in valid_colors))
+ print(f"\n--- Unique Hex Values ({len(unique_hexes)}) ---")
+ for h in unique_hexes:
+ rgb = hex_to_rgb(h)
+ hsl = rgb_to_hsl(*rgb)
+ cl = classify_color(h)
+ tag = "GENERIC" if cl['is_generic'] else "specific"
+ count = sum(1 for c in valid_colors if c['hex'] == h)
+ print(f" {h} RGB({rgb[0]:3d},{rgb[1]:3d},{rgb[2]:3d}) "
+ f"HSL({hsl[0]:5.1f},{hsl[1]:4.1f}%,{hsl[2]:4.1f}%) "
+ f"x{count} [{tag}, near {cl['nearest_primary']}, d={cl['primary_distance']}]")
+
+ # Distance statistics
+ distances = [c['primary_distance'] for c in valid_colors]
+ avg_dist = sum(distances) / len(distances)
+ min_dist = min(distances)
+ max_dist = max(distances)
+ print(f"\n--- Distance from Nearest Primary ---")
+ print(f" Min: {min_dist:.1f} Avg: {avg_dist:.1f} Max: {max_dist:.1f}")
+ print(f" (Higher = more specific. Threshold for 'generic' = {GENERIC_DISTANCE_THRESHOLD})")
+
+ # Verdict
+ print(f"\n--- Verdict ---")
+ if pct_specific >= 80:
+ print(f" The model returns SPECIFIC hex colors ({pct_specific:.0f}% distinct shades).")
+ print(f" It is capable of estimating precise colors from images.")
+ elif pct_specific >= 40:
+ print(f" MIXED results ({pct_specific:.0f}% specific).")
+ print(f" The model sometimes returns specific shades but often falls back to primaries.")
+ else:
+ print(f" The model mostly returns GENERIC primary colors ({pct_specific:.0f}% specific).")
+ print(f" Hex codes are largely just the standard primary color equivalents.")
+
+
+if __name__ == '__main__':
+ main()
\ No newline at end of file