Create app.py

app.py

@@ -0,0 +1,135 @@
+import numpy as np
+import gradio as gr
+import math
+
+demo = gr.Blocks()
+
+def estimate(input_img):
+  threshold = 0.9
+  img  = Image.fromarray(input_img.astype('uint8'),'RGB')
+
+  (w, h) = img.size
+  image_intrinsics = np.array([[w + h, 0, w // 2], [0, w + h, h // 2], [0, 0, 1]])
+      
+  res = img2pose_model.predict([transform(img)])[0]
+
+  all_bboxes = res["boxes"].cpu().numpy().astype('float')
+
+  poses = []
+  bboxes = []
+  for i in range(len(all_bboxes)):
+      if res["scores"][i] > threshold:
+          bbox = all_bboxes[i]
+          pose_pred = res["dofs"].cpu().numpy()[i].astype('float')
+          pose_pred = pose_pred.squeeze()
+
+          poses.append(pose_pred)  
+          bboxes.append(bbox)
+
+  trans_vertices = renderer.transform_vertices(img, poses)
+  img = renderer.render(img, trans_vertices, alpha=1)    
+
+  plt.figure(figsize=(8, 8))     
+    
+  for bbox in bboxes:
+      plt.gca().add_patch(patches.Rectangle((bbox[0], bbox[1]), bbox[2] - bbox[0], bbox[3] - bbox[1],linewidth=3,edgecolor='b',facecolor='none'))            
+    
+  plt.imshow(img)        
+  plt.show()
+  pose_pred[0] = math.degrees(pose_pred[0])
+  pose_pred[1] = math.degrees(pose_pred[1])
+  pose_pred[2] = math.degrees(pose_pred[2])
+  listToStr = ' '.join([str(elem) for elem in pose_pred])
+  return img,pose_pred[0],pose_pred[1],pose_pred[2],pose_pred[3],pose_pred[4],pose_pred[5]
+
+
+def estimate_crowd(input_img):
+  threshold = 0.5
+  img  = Image.fromarray(input_img.astype('uint8'),'RGB')
+
+  (w, h) = img.size
+  image_intrinsics = np.array([[w + h, 0, w // 2], [0, w + h, h // 2], [0, 0, 1]])
+  
+  res = img2pose_model.predict([transform(img)])[0]
+
+  all_bboxes = res["boxes"].cpu().numpy().astype('float')
+
+  poses = []
+  bboxes = []
+  for i in range(len(all_bboxes)):
+      if res["scores"][i] > threshold:
+          bbox = all_bboxes[i]
+          pose_pred = res["dofs"].cpu().numpy()[i].astype('float')
+          pose_pred = pose_pred.squeeze()
+
+          poses.append(pose_pred)  
+          bboxes.append(bbox)
+
+
+  #render_plot(img.copy(), poses, bboxes)
+  
+  n = str((len(bboxes)))
+  (w, h) = img.size
+  image_intrinsics = np.array([[w + h, 0, w // 2], [0, w + h, h // 2], [0, 0, 1]])
+    
+  trans_vertices = renderer.transform_vertices(img, poses)
+  img = renderer.render(img, trans_vertices, alpha=1) 
+  return img, n
+
+
+with demo:
+
+    gr.Markdown("Head Pose Estimation in Crowd")
+
+    with gr.Tabs():
+        with gr.TabItem("What is Head Pose Estimation?"):
+           gr.Markdown(
+        """
+            ### Head pose estimation in crowd (HPECS) is a task of estimating the head pose of people in the crowd scenes.
+
+            Six degrees of freedom of head pose is the freedom of movement of the head pose in three-dimensional space. 6DoF is the 
+            abbreviation of six degrees of freedoms. 6DoF of head pose records the rotational motion such as
+            pitch, yaw and roll , and translational motions of the head pose including up/down, left/right and front/back. 
+            6DoF of 3D head pose is being regressed as it can harvest more versatile 
+            spatial information which able to track the movement of object even in a still image. 
+
+    """
+          )
+           gr.Image("/content/gdrive/MyDrive/project_folder/img2pose/6DoF.gif",size=20)
+
+ 
+        with gr.TabItem("Head Pose Estimation"):
+            gr.Markdown(
+        """
+            ### Please insert image with single head
+
+    """
+          )
+            image = gr.inputs.Image()
+            text_button = gr.Button("Estimate")
+            with gr.Row():
+              outputs=[gr.outputs.Image(),gr.outputs.Textbox(type="auto", label="Yaw"),
+                                              gr.outputs.Textbox(type="auto", label="Pitch"),
+                                              gr.outputs.Textbox(type="auto", label="Roll"),
+                                              gr.outputs.Textbox(type="auto", label="tx"),
+                                              gr.outputs.Textbox(type="auto", label="ty"),
+                                              gr.outputs.Textbox(type="auto", label="tz")]
+            
+        with gr.TabItem("Estimate Head Pose in Crowd"):
+            gr.Markdown(
+          """
+              ### Please insert crowd scene image
+
+      """
+            )
+            image_input = gr.Image()
+            with gr.Row():
+                
+                image_output = [gr.outputs.Image(),gr.outputs.Textbox(type="auto", label="Number of head pose estimated"),
+                                            ]
+            image_button = gr.Button("Estimate")
+
+    text_button.click(estimate, inputs=image, outputs=outputs)
+    image_button.click(estimate_crowd, inputs=image_input, outputs=image_output)
+
+demo.launch()