Update README.md

README.md

@@ -75,7 +75,121 @@ If you want to replicate the annotation setup, the steps are outlined at the [bo
 
 This dataset and the annotation process is described in further detail in our blog post [Beyond Image Preferences](https://huggingface.co/blog/RapidataAI/beyond-image-preferences).
 
-# Word Scores
+# Usage Examples
+Accessing this data is easy with the Huggingface `dataset` library. For quick demos or previews, we recommend setting `streaming=True` as downloading the whole dataset can take a while.
+
+```python
+  from datasets import load_dataset
+
+  ds = load_dataset("Rapidata/text-2-image-Rich-Human-Feedback", split="train", streaming=True)
+```
+
+As an example, below we show how to replicate the figures below.
+
+<details>
+<summary>Click to expand Select Words example</summary>
+The methods below can be used to produce figures similar to the ones shownn below. 
+Note however that the figures below were created using `matplotlib`, however we opt to use `opencv` here as it makes calculating the text spacing much easier.
+  
+**Methods**
+```python
+from PIL import Image
+from datasets import load_dataset
+import cv2 
+import numpy as np
+
+def get_colors(words):
+    colors = []
+    for item in words:
+        intensity = item / max(words)
+        value = np.uint8((1 - intensity) * 255)
+        color = tuple(map(int, cv2.applyColorMap(np.array([[value]]), cv2.COLORMAP_AUTUMN)[0][0]))
+        colors.append(color)
+    return colors
+
+def get_wrapped_text(text_color_pairs, font, font_scale, thickness, word_spacing, max_width):
+    wrapped_text_color_pairs, current_line, line_width = [], [], 0
+    for text, color in text_color_pairs:
+        text_size = cv2.getTextSize(text, font, font_scale, thickness)[0]
+        if line_width + text_size[0] > max_width:
+            wrapped_text_color_pairs.append(current_line)
+            current_line, line_width = [], 0
+        current_line.append((text, color, text_size))
+        line_width += text_size[0] + word_spacing
+    wrapped_text_color_pairs.append(current_line)
+    return wrapped_text_color_pairs
+
+def add_multicolor_text(input, text_color_pairs, font_scale=1, thickness=2, word_spacing=20):
+    image = cv2.cvtColor(np.array(input), cv2.COLOR_RGB2BGR)
+    image_height, image_width, _ = image.shape
+
+    font = cv2.FONT_HERSHEY_SIMPLEX
+    wrapped_text = get_wrapped_text(text_color_pairs, font, font_scale, thickness, word_spacing, int(image_width*0.95))
+
+    position = (int(0.025*image_width), int(word_spacing*2))
+    
+    overlay = image.copy()
+    cv2.rectangle(overlay, (0, 0), (image_width, int((len(wrapped_text)+1)*word_spacing*2)), (100,100,100), -1)
+    out_img = cv2.addWeighted(overlay, 0.75, image, 0.25, 0)
+
+    for idx, text_line in enumerate(wrapped_text):
+        current_x, current_y = position[0], position[1] + int(idx*word_spacing*2)
+        for text, color, text_size in text_line:
+            cv2.putText(out_img, text, (current_x, current_y), font, font_scale, color, thickness)
+            current_x += text_size[0] + word_spacing
+
+    return Image.fromarray(cv2.cvtColor(out_img, cv2.COLOR_BGR2RGB))
+```
+**Create figures**
+```python
+ds_words = ds.select_columns(["image","prompt", "word_scores"])
+
+for example in ds_words.take(5):
+    image = example["image"]
+    prompt = example["prompt"]
+    word_scores = [s[1] for s in eval(example["word_scores"])]
+    words = [s[0] for s in eval(example["word_scores"])]
+    colors = get_colors(word_scores)
+    display(add_multicolor_text(image, list(zip(words, colors)), font_scale=1, thickness=2, word_spacing=20))
+```
+</details>
+
+<details>
+  <summary>Click to expand Heatmap example</summary>
+  
+  **Methods**
+  ```python
+  import cv2
+  import numpy as np
+  from PIL import Image
+  
+  def overlay_heatmap(image, heatmap, alpha=0.3):
+      cv2_image =  cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)   
+      heatmap_normalized = ((heatmap - heatmap.min()) / (heatmap.max() - heatmap.min()))
+      heatmap_normalized = np.uint8(255 * (heatmap_normalized))
+      heatmap_colored = cv2.applyColorMap(heatmap_normalized, cv2.COLORMAP_HOT)
+      overlaid_image = cv2.addWeighted(cv2_image, 1 - alpha, heatmap_colored, alpha, 0)
+      
+      return Image.fromarray(cv2.cvtColor(overlaid_image, cv2.COLOR_BGR2RGB))
+```
+**Create figures**
+```python
+  ds_heatmap = ds.select_columns(["image","prompt", "alignment_heatmap"])
+  
+  for example in ds_heatmap.take(5):
+      image = example["image"]
+      heatmap = example["alignment_heatmap"]
+      if heatmap:
+          display(overlay_heatmap(image, np.asarray(heatmap)))
+  ```
+  
+</details>
+
+</br>
+
+# Data Summary
+
+## Word Scores
 Users identified words from the prompts that were NOT accurately depicted in the generated images. Higher word scores indicate poorer representation in the image. Participants also had the option to select "[No_mistakes]" for prompts where all elements were accurately depicted.
 
 ### Examples Results:
@@ -84,7 +198,7 @@ Users identified words from the prompts that were NOT accurately depicted in the
 | <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/4uWKVjZBA5aX2YDUYNpdV.png" width="500"> | <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/f9JIuwDoNohy7EkDYILFm.png" width="500"> |
 
 
-# Coherence
+## Coherence
 The coherence score measures whether the generated image is logically consistent and free from artifacts or visual glitches. Without seeing the original prompt, users were asked: "Look closely, does this image have weird errors, like senseless or malformed objects, incomprehensible details, or visual glitches?" Each image received at least 21 responses indicating the level of coherence on a scale of 1-5, which were then averaged to produce the final scores where 5 indicates the highest coherence.
 
 Images scoring below 3.8 in coherence were further evaluated, with participants marking specific errors in the image.
@@ -96,7 +210,7 @@ Images scoring below 3.8 in coherence were further evaluated, with participants
 | <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/mRDdoQdc4_iy2JcLhdI7J.png" width="500"> | <img src="https://cdn-uploads.huggingface.co/production/uploads/672b7d79fd1e92e3c3567435/2N2KJyz4YOGT6N6tuUX8M.png" width="500"> |
 
 
-# Alignment
+## Alignment
 The alignment score quantifies how well an image matches its prompt. Users were asked: "How well does the image match the description?". Again, each image received at least 21 responses indicating the level of alignment on a scale of 1-5 (5 being the highest), which were then averaged.
 
 For images with an alignment score below 3.2, additional users were asked to highlight areas where the image did not align with the prompt. These responses were then compiled into a heatmap.
@@ -105,7 +219,6 @@ As mentioned in the google paper, aligment is harder to annotate consistently, i
 
 ### Example Results:
 
-
 <style>
 .example-results-grid {
     display: grid;
@@ -181,8 +294,7 @@ As mentioned in the google paper, aligment is harder to annotate consistently, i
     </div>
 </div>
 
-
-# Style
+## Style
 The style score reflects how visually appealing participants found each image, independent of the prompt. Users were asked: "How much do you like the way this image looks?" Each image received 21 responses grading on a scale of 1-5, which were then averaged. 
 In contrast to other prefrence collection methods, such as the huggingface image arena, the preferences were collected from humans from around the world (156 different countries) from all walks of life, creating a more representative score.