Update app.py
Browse files
app.py
CHANGED
@@ -18,20 +18,6 @@ def create_monitor_interface():
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self.max_image_size = (800, 800)
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self.colors = [(0, 0, 255), (255, 0, 0), (0, 255, 0), (255, 255, 0), (255, 0, 255)]
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def resize_image(self, image):
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height, width = image.shape[:2]
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if height > self.max_image_size[1] or width > self.max_image_size[0]:
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aspect = width / height
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if width > height:
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new_width = self.max_image_size[0]
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new_height = int(new_width / aspect)
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else:
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new_height = self.max_image_size[1]
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new_width = int(new_height * aspect)
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return cv2.resize(image, (new_width, new_height), interpolation=cv2.INTER_AREA)
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return image
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def analyze_frame(self, frame: np.ndarray) -> str:
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if frame is None:
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return ""
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@@ -57,19 +43,29 @@ def create_monitor_interface():
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completion = self.client.chat.completions.create(
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model=self.model_name,
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messages=[
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": """Analyze this image for safety concerns. For each
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},
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{
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"type": "image_url",
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@@ -78,23 +74,34 @@ def create_monitor_interface():
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}
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}
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]
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},
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{
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"role": "assistant",
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"content": ""
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}
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],
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temperature=0.
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max_tokens=500,
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stream=False,
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stop=None
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)
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except Exception as e:
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print(f"Analysis error: {str(e)}")
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return ""
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def get_region_coordinates(self, position: str, image_shape: tuple) -> tuple:
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height, width = image_shape[:2]
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regions = {
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@@ -109,10 +116,19 @@ def create_monitor_interface():
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'bottom-right': (2*width//3, 2*height//3, width, height)
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}
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return regions['center']
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def draw_observations(self, image, observations):
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@@ -128,27 +144,23 @@ def create_monitor_interface():
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if len(parts) >= 2:
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position = parts[0]
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description = ':'.join(parts[1:])
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(label_x + label_size[0], label_y), color, -1)
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# Draw text
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cv2.putText(image, label, (label_x, label_y - 5),
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font, font_scale, (255, 255, 255), thickness)
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return image
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@@ -157,8 +169,8 @@ def create_monitor_interface():
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return None, "No image provided"
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analysis = self.analyze_frame(frame)
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# Parse observations
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observations = []
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for line in analysis.split('\n'):
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line = line.strip()
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@@ -170,14 +182,19 @@ def create_monitor_interface():
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if observation and ':' in observation:
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observations.append(observation)
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display_frame = frame.copy()
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if observations:
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annotated_frame = self.draw_observations(display_frame, observations)
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return annotated_frame, analysis
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# Create the main interface
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monitor = SafetyMonitor()
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with gr.Blocks() as demo:
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@@ -205,13 +222,6 @@ def create_monitor_interface():
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outputs=[output_image, analysis_text]
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)
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gr.Markdown("""
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## Instructions:
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1. Upload an image to analyze
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2. View identified safety concerns with bounding boxes
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3. Read detailed analysis results
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""")
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return demo
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demo = create_monitor_interface()
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self.max_image_size = (800, 800)
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self.colors = [(0, 0, 255), (255, 0, 0), (0, 255, 0), (255, 255, 0), (255, 0, 255)]
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def analyze_frame(self, frame: np.ndarray) -> str:
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if frame is None:
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return ""
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completion = self.client.chat.completions.create(
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model=self.model_name,
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messages=[
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{
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"role": "system",
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"content": "You are a safety analysis expert. Analyze images for safety concerns and provide detailed observations."
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},
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": """Analyze this image for safety concerns and risks. For each issue you identify:
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1. Specify the exact location in the image where the issue is visible
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2. Describe what the safety concern is
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3. Include any relevant details about PPE, posture, equipment, or environmental hazards
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Format EACH observation exactly like this:
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- <location>position:detailed description of the concern</location>
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Example format:
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- <location>center:Worker bending incorrectly while lifting heavy materials</location>
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- <location>top-right:Missing safety guardrail near elevated platform</location>
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Provide multiple observations if you see multiple issues."""
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},
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{
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"type": "image_url",
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}
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}
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]
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}
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],
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temperature=0.5, # Increased for more varied observations
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max_tokens=500,
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stream=False
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)
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response = completion.choices[0].message.content
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print(f"Raw response: {response}") # For debugging
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return response
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except Exception as e:
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print(f"Analysis error: {str(e)}")
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return ""
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def resize_image(self, image):
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height, width = image.shape[:2]
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if height > self.max_image_size[1] or width > self.max_image_size[0]:
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aspect = width / height
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if width > height:
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new_width = self.max_image_size[0]
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new_height = int(new_width / aspect)
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else:
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new_height = self.max_image_size[1]
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new_width = int(new_height * aspect)
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return cv2.resize(image, (new_width, new_height), interpolation=cv2.INTER_AREA)
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return image
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def get_region_coordinates(self, position: str, image_shape: tuple) -> tuple:
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height, width = image_shape[:2]
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regions = {
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'bottom-right': (2*width//3, 2*height//3, width, height)
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}
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# Try to match the position with regions
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matched_region = None
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max_match_length = 0
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position_lower = position.lower()
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for region_name in regions:
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if region_name in position_lower:
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if len(region_name) > max_match_length:
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matched_region = region_name
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max_match_length = len(region_name)
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if matched_region:
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return regions[matched_region]
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return regions['center']
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def draw_observations(self, image, observations):
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if len(parts) >= 2:
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position = parts[0]
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description = ':'.join(parts[1:])
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x1, y1, x2, y2 = self.get_region_coordinates(position, image.shape)
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# Draw rectangle
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cv2.rectangle(image, (x1, y1), (x2, y2), color, 2)
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# Add label with background
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label = description[:50] + "..." if len(description) > 50 else description
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label_size = cv2.getTextSize(label, font, font_scale, thickness)[0]
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label_x = max(0, min(x1, width - label_size[0]))
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label_y = max(20, y1 - 5)
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cv2.rectangle(image, (label_x, label_y - 20),
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(label_x + label_size[0], label_y), color, -1)
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cv2.putText(image, label, (label_x, label_y - 5),
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font, font_scale, (255, 255, 255), thickness)
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return image
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return None, "No image provided"
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analysis = self.analyze_frame(frame)
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print(f"Analysis received: {analysis}") # Debug print
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observations = []
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for line in analysis.split('\n'):
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line = line.strip()
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if observation and ':' in observation:
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observations.append(observation)
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print(f"Parsed observations: {observations}") # Debug print
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display_frame = frame.copy()
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if observations:
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annotated_frame = self.draw_observations(display_frame, observations)
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return annotated_frame, analysis
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# If no observations were found but we got some analysis
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if analysis and not analysis.isspace():
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return display_frame, analysis
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return display_frame, "Please try again - no safety analysis was generated."
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monitor = SafetyMonitor()
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with gr.Blocks() as demo:
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outputs=[output_image, analysis_text]
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)
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return demo
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demo = create_monitor_interface()
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